System and method of extending the linear dynamic range of event counting

ABSTRACT

A method and apparatus for photon, ion or particle counting described that provides seven orders of magnitude of linear dynamic range (LDR) for a single detector. By explicitly considering the log-normal probability distribution in voltage transients as a function of the number of photons, ions or particles present, the binomial distribution of observed counts for a given threshold, the mean number of photons, ions or particles can be determined well beyond the conventional limit.

RELATED APPLICATIONS

This application claims the benefit of priority to U.S. provisionalapplications 61/386,735 filed on Sep. 27, 2010 and 61/457,948, filed onJul. 14, 2011.

STATEMENT OF GOVERNMENT SUPPORT

This invention was made with government support under grant numberGM088499 awarded by National Institutes of Health. The government hascertain rights in the invention.

TECHNICAL FIELD

This application may relate to the detection of particles usingdetectors with statistical properties.

BACKGROUND

Photon counting is a well-known method detecting low intensity light.However, existing approaches for photon counting suffer fromnonlinearities at high photon count rates. Several strategies have beenadopted for improving the linear dynamic range (LDR) of light detectionusing photomultiplier tube (PMT) and avalanche photodiode detectors(APD), or other detector types having similar statistic properties.

For example, neutral density filters can attenuate high light levels soas to remain within the linear range of counting systems. The responsetime of this technique is not fast enough to provide large continuousdynamic ranges in rapid sampling applications and requires carefulcalibration of optical density for the filters. Another method uses aplurality of photo detectors and fiber-optic beam splitters\to sampledifferent fractions of the beam, equivalent to performing simultaneousphoton counting with several neutral density filters. Using thisapproach, 6 orders of magnitude of linear been response has beenachieved.

Other methods include the combining of photon-counting detection forlow-light levels with analog-to-digital conversion (ADC) so as to extendthe LDR to the high photon flux regime. Another method includes fast ADCof the temporal time-trace followed by Fourier transformation todeconvolve the number of photons present in a time window, but thisrequires long analysis times and fast ADC (˜1 GHz sampling).

Each of these approaches involves performance trade-offs. Detectorsoptimized for photon counting with fast rise/fall times are generallynot optimized for ADC and vice versa. Sensitivity mismatch in theinstrument responses from single photon counting (SPC) with ADC mayimpact reliable quantitation and may require simultaneous dataacquisition using two fundamentally different electronics approaches.The noise contribution from combinations of multiple detectors isadditive. In addition, differences in sensitivity and drift maycompromise the accuracy when stitching together the results frommultiple detectors.

The relationship between the detected count rate and the selection ofthe threshold voltage of a counting discriminator(s) has been studied.Use of multiple thresholds to improve the dynamic range of photoncounting systems from a single-channel detector has been demonstrated Inmeasurements with pulsed excitation and long times between pulsesrelative to the detector response time (e.g., multi-photon and nonlinearoptical microscopy at <100 MHz laser pulse repetition rates withdetector fall times <10 ns); the voltage transients from thesingle-photon events can be reliably treated as temporally coincident.Detection of up to 4 simultaneous photons per laser pulse was achievedby careful adjustment of the detection voltage threshold of eachdiscriminator to fall between the peak voltage distributions of n andn+1 simultaneous photons. This approach suffers in practice from therelatively large intrinsic variations in peak voltage distributions fora single photon in most practical photomultiplier tubes. Since the meanand variance in the peak voltage distribution increase linearly with thenumber of photons, the distributions for and n and n+1 photons quicklyoverlap as n increases, rapidly increasing the uncertainty in attemptsto quantify the number of simultaneous photons with this approach.

SUMMARY

A method for analyzing data acquired by using discriminator-based eventcounting electronics to measure a count output of a detector isdisclosed, including: relating a binomially distributed measurement ofcounts and a Poisson distributed signal of discreet events; determininga Poisson-weighted detector response function; and using thePoisson-weighted detector response to use the binomially distribution ofcounts and the Poisson distributed signal of discrete events so as todetermine an estimate of a number of discrete generating events.

In an aspect, each count is a signal having a voltage value and themethod includes synchronizing an analog-to-digital conversion of thesignals such that the analog-to-digital conversion is performed at atime where the signal is expected; performing analog-to-digitalconversion of the signal from the detector, and analyzing an amplitudedistribution of a plurality of digitized signals to connect the binomialand Poisson distributions so as to determine an estimate of number ofdiscrete generating events.

A computer program product, stored on a non-transient computer readablemedium is disclosed, having instructions for operating a computer systemso as to: synchronize an analog-to-digital conversion of an output of adetector such that the analog-to-digital conversion is performed at atime where a signal is expected; and analyze an amplitude distributionof a plurality of digitized signals to connect the binomial and Poissondistributions so as to determine an estimate of number of discretegenerating events.

An apparatus for counting detected events is disclosed, having a pulsedlight source; a detector; an analog-to-digital converter having asampling rate synchronized with the pulsed light source. A processor isconfigured to accept an analog-to-digital converter output data and toprocess the data so as to combine the measurements of count-type dataand average signal-type data so as to linearize the estimate of discretegenerating events over a range of discrete generating events.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1, A, shows representative calculated lognormal voltagedistributions for n photon events based on the same initial parameters,M_(L,1) and S_(L,1). M_(L,n) and S_(L,n) calculated using equations (10)and (11); and. B, the CPHD for various mean number of photons per trial.Each curve integrates to p instead of 1 because no voltage is generatedfor zero photons;

FIG. 2 shows an example relating Poisson distribution of photons andbinomial probability, p;

FIG. 3 shows the signal to noise ratios (μ/σ) calculated using equations(15) and (16) and theoretical maximum based on the Poisson distributionare compared for N=100,000 trials. The vertical bars mark the p valuesthat correspond to given means, and the multiplier indicates theincrease in noise relative to the theoretical limit;

FIG. 4 shows the value of p for different threshold settings as afunction of the mean number of photons. Different thresholds havesensitivity to different photon ranges, and the overlap in these curvesresults in continuous quantitation;

FIG. 5 is a representation of second harmonic generation opticalequipment configuration;

FIG. 6 shows peak voltage distributions for two incident photon fluxes.The solid lines are the best fit theoretical Poisson weighted sum oflognormal distributions with parameters determined from single photonvoltage distributions. The green points and pink line are rescaled by afactor of ten for visualization;

FIG. 7 shows a comparison of observed counts (triangles) and calculatedphotons (diamonds) outside of the Poisson counting range (λ>0.103) ascalculated by the single threshold method in equation (15);

FIG. 8 shows a comparison of conventional SPC (triangles),signal-averaging ADC (squares) and calculated photons (diamonds) usingmultiple counting threshold settings. The ADC measurements were rescaledto overlay to account for the proportionality between photons andvoltage;

FIG. 9 shows histograms of voltage peak distributions measured at singlepixel locations in an image, where A is a pixel with no signal countsand dominated by Johnson noise, and, B, is a pixel with sparse counts.The two images are shown on different voltage scales; and

FIG. 10 shows images resulting from statistical analysis of the peakheight distributions of raster scan microscopy data, where. A, is photoncounting image with a threshold selected based on the measuredone-photon peak height distribution and the Johnson noise. Each pixelhas units of counts measured by discrimination; B, is a signal-averagedimage, in which each pixel is the mean detector voltage; and C, astitched image with the signal-to-noise optimized at each pixel usingeither the discrimination or the mean values (the bottom image iscomprised of the mean of the Poisson distribution reported in eachpixel, with a continuous dynamic range spanning a mean of 0.002 photonsper pulse to a mean of 75 photons per pulse (i.e., spanning thelow-light limit set by dark counts up to 5.9 billion photons per secondin this case).

DETAILED DESCRIPTION

Exemplary embodiments may be better understood with reference to thedrawings, but these embodiments are not intended to be of a limitingnature. In the following description, specific details are set forth inorder to provide a thorough understanding of the present inventionwhich, however, may be practiced without some or all of these specificdetails. In other instances, well known process operations have not beendescribed in detail in order not to unnecessarily obscure thedescription.

It will be appreciated that the methods described and the apparatusshown in the figures may be configured or embodied in machine-executableinstructions, e.g. software, or in hardware, or in a combination ofboth. The instructions can be used to cause a general-purpose computer,a special-purpose processor, such as a DSP or array processor, or thelike, that is programmed with the instructions to perform the operationsdescribed. Alternatively, the operations might be performed by specifichardware components that contain hardwired logic or firmwareinstructions for performing the operations described, or by anycombination of programmed computer components and custom hardwarecomponents, which may include analog circuits.

The methods may be provided, at least in part, as a computer programproduct that may include a non-transient machine-readable medium havingstored thereon instructions which may be used to program a computer (orother electronic devices) to perform the methods. For the purposes ofthis specification, the terms “machine-readable medium” shall be takento include any medium that is capable of storing a sequence ofinstructions or data for execution by a computing machine orspecial-purpose hardware and that cause the machine or special purposehardware to perform any one of the methodologies or functions of thepresent invention. The term “machine-readable medium” shall accordinglybe taken include, but not be limited to, solid-state memories, opticaland magnetic disks, magnetic memories and optical memories. Thedescription of a method as being performed by a computer should notpreclude a portion of the same method being performed by a person.

For example, but not by way of limitation, a machine readable medium mayinclude read-only memory (ROM); random access memory (RAM) of all types(e.g., S-RAM, D-RAM. P-RAM); programmable read only memory (PROM);electronically alterable read only memory (EPROM); magnetic randomaccess memory; magnetic disk storage media; and, flash memory.

Furthermore, it is common in the art to speak of software, in one formor another (e.g., program, procedure, process, application, module,algorithm or logic), as taking an action or causing a result. Suchexpressions are merely a convenient way of saying that execution of thesoftware by a computer or equivalent device causes the processor of thecomputer or the equivalent device to perform an action or a produce aresult, as is well known by persons skilled in the art.

When describing a particular example, the example may include aparticular feature, structure, or characteristic, but every example maynot necessarily include the particular feature, structure orcharacteristic. This should not be taken as a suggestion or implicationthat the features, structure or characteristics of two or more examplesshould not or could not be combined, except when such a combination isexplicitly excluded. When a particular feature, structure, orcharacteristic is described in connection with an example, a personskilled in the art may give effect to such feature, structure orcharacteristic in connection with other examples, whether or notexplicitly described.

An analytical expression is described that may be used in an apparatusand method to extend the linear range of traditional (single threshold)photon counting measurements to ˜74 simultaneous photons, and amulti-threshold detection method is described that may extend LDR rangeup to that of ADC signal averaging. The inability of voltagediscrimination to reliably distinguish between single photon andsimultaneous multiple photon events, which was limiting in the previoustechniques is overcome by the technique described herein.

After determining the expected single-photon peak-height distribution,convolved peak-height distributions for multiple photon events arecalculated, and then summed by a Poisson weighting to generate the netcollective peak height distribution (CPHD) for an assumed mean of aPoisson distribution. The mean number of counts expected for a givendiscriminator threshold is calculated from the CPHD and compared withexperimentally measured counts exceeding the threshold. Values for themean of the Poisson distribution are then iteratively adjusted by asingle-parameter weighted least squares minimization.

An objective of quantitative photon counting measurements may be theaccurate determination of the mean of the underlying Poissondistribution, f_(P).

$\begin{matrix}{{f_{P}x} = {\frac{\lambda^{x}}{x!}e^{- \lambda}}} & (1) \\{\mu_{P} = {{\lambda\mspace{20mu}\sigma_{P}^{2}} = \lambda}} & (2)\end{matrix}$

However, the use of discriminators in the detection process yields twopossible outcomes: either the voltage transient exceeds a threshold orit does not. Therefore, the output of conventional photon-countinginstrumentation is described by a binomial probability density function(pdf). Here, the probability f_(B) of observing a particular number ofcounts x for a given discriminator threshold in a photon countingexperiment follows the binomial distribution.

$\begin{matrix}{{f_{B}x} = {{\frac{N!}{{{x!}N} - {x!}}p^{x}1} - p^{x}}} & (3) \\{\mu_{B} = {{N\; p\mspace{14mu}\sigma_{B}^{2}} = {{N\; p\; 1} - p}}} & (4)\end{matrix}$

where μ is the mean and σ is

the standard deviation.

In (3), p is the probability of a successful outcome (e.g., a voltageexceeding a discriminator threshold), and N is the number ofmeasurements. In the limit of low values of p and high values of Nbinomial distribution converges to the Poisson, allowing μ_(B) (e.g. themeasured photon counts) to be used as μ_(P). However, these twodistributions diverge as μ_(P) increases and the number of events inwhich two or more photons are generated per laser pulse becomessignificant. Most conventional photon counting approaches are restrictedto this low-flux regime where the two distributions converge. However,the binomial distribution is still valid for describing experimentalmeasurements with higher count rates.

The method and apparatus described herein permits determining the meanof the underlying Poisson distribution of incident photons from themeasured binomially distributed values. This may extend the range overwhich Poisson-distributed counts can be recorded by photon countingmethods. The signal-to-noise ratio (SNR) is maintained close to thetheoretical Poisson limit of conventional photon counting for photonarrival rates over nearly the entire range of measurement. Thediscussion is focused on pulsed-mode operation, such as might arise inmulti-photon or nonlinear optical microscopy using high-repetition rateultrafast laser sources. However, the approach is general to anycounting measurement that is based on electron multiplication, orsimilar effect.

The distribution in detected peak voltages and the number of photonsarriving at the detector may be deduced from the nature of theamplification process. Each photon incident on a PMT ejects a singlephotoelectron from the photocathode. That ejected electron is ejectedtowards the first dynode. When the electron strikes the first dynode,the electron imparts sufficient energy to release several more electronswhich, are accelerated towards the second dynode. This process continuesuntil a large burst of electrons reaches the anode, generating currenttransients large enough to be discriminated from the background noise.At each dynode there is a random amplification factor or gain thatdescribes the probability of a given number of electrons being ejectedfor each incident accelerated electron from the preceding dynode.

The total gain per initial photoelectron (V) is a product of the gain ateach dynode. Since the gain at each dynode is an independent, randomnumber, the pdf of V converges to a lognormal distribution in accordancewith the Central Limit Theorem for multiplicative processes.

The log-normal peak voltage distribution for a single initial electron,f_(L,1) with scale and shape parameters M_(L,1) and S_(L,1), generatedby an electron multiplying device may be expressed as:

$\begin{matrix}{{f_{L,1}V} = {\frac{1}{\overset{\_}{2\pi}S_{L,1}V}e^{\frac{- {({{lnV} - M_{L,1}})}^{2}}{2\pi\; S_{L,1}^{2}}}}} & (5) \\{\mu_{L,1} = e^{M_{L,1} + {S_{L,1}^{2}2}}} & (6) \\{\sigma_{L,1}^{2} = {{e^{{2M_{L,1}} + S_{L,1}^{2}}e^{S_{L,1}^{2}}} - 1}} & (7)\end{matrix}$

In the event photons, n where n>=2), arrive at the detectorsimultaneously, the peak voltage generated will be a sum of n randomnumbers from the single photon voltage distribution. The pdf for an nphoton event is given by n−1 convolutions of the single photon responsewith itself.f _(L,n) =f _(L,1)

f _(L,1) ^(n−1)  (8)

Although no analytical form is known for even the first convolution oftwo lognormal pdfs, the resulting pdfs from the convolutions can oftenbe remarkably well-approximated as lognormal under the conditionstypical of photon/electron counting. Using the expressions for thepropagated mean and variance of the sum of n statistically independentrandom numbers,μ_(L,n) =nμ _(L,1) σ_(L,n) ² =nσ _(L,1) ²  (9)and the expressions given in (8) for a lognormal pdf, rearrangement ofthe equations yields the following expressions for the shape parametersM_(L,n) and S_(L,n) for the lognormal pdf after n−1 convolutions:

$\begin{matrix}{M_{L,n} = {\ln\frac{\;{n^{3}e^{{2M_{L,1}} + S_{L,1}^{2}}}}{n - e^{S_{L,1}^{2} - 1}}}} & (10) \\{S_{L,n} = \overset{\_}{\ln\;\frac{n + e^{S_{L,1}^{2}} - 1}{n}}} & (11)\end{matrix}$

The theoretical pdfs for multiple photon events are shown in FIG. 1using representative values for the mean and standard deviation of thesingle-photon lognormal distribution.

Taking account of both the Poisson distribution of photons and thelog-normally distributed peak voltages associated with a number ofphotons, n, incident on the PMT, the value of p (i.e., the probabilityof a successful observation of a count) in (3) can be expressed, for anythreshold, as:

$\begin{matrix}{p_{threshold} = {\begin{matrix}\infty \\{n = 1}\end{matrix}\frac{\lambda_{n}}{n!}e^{- \lambda}\mspace{11mu}\begin{matrix}\infty \\{threshold}\end{matrix}\frac{1}{\overset{\_}{2\pi}S_{L,n}V}e^{\frac{- {({{lnV} - M_{L,n}})}^{2}}{2\pi\; S_{L,n}^{2}}}d\; V}} & (12)\end{matrix}$

In practice, the threshold in the above expression is adjusted as low aspossible such that the integral for any n goes to 1 (i.e., approachingthe limit in which every time one or more photon Initiates an electroncascade), every resulting voltage transient is expected to exceed thethreshold. This theoretical limit may be closely approximatedexperimentally with appropriate detectors, and data processing. In thislimit, evaluation of the integral is no longer required, leaving onlythe Poisson probabilities for n photons. Np then increases linearlyuntil the Poisson probability for n>1 becomes significant. When λ≈0.103(p=0.098) or higher, the binomial observable, Np, will underestimate thetrue number of photons by about 5%, representing the practical upperlimit of the linear range for conventional photon counting. FIG. 2demonstrates that the observed counts are lower than the true number ofphotons. The binomial parameter p, which in this plot is 0.9, describesthe integrated probability of observing one or more photons. As many asabout 8 photons may arrive at the detector simultaneously under theseconditions, placing the number of counts outside linear photon countingrange.

An analytical expression can be derived that relates the counts andphotons that takes advantage of the Poisson statistics afforded bysimultaneous photon arrival. If the threshold is low enough, theexpression for p converges to the Poisson cumulative distributionfunction (cdf):

$\begin{matrix}{p = {{\begin{matrix}\infty \\{n = 1}\end{matrix}\frac{\lambda^{n}}{n!}e^{- \lambda}} = {1 - e^{- \lambda}}}} & (13)\end{matrix}$

The expression for the most probable value of λ and the variance in thevalue in terms of observed counts can be derived using this expressionfor Np as the fitting function in a weighted one parameter nonlinearfit.

$\begin{matrix}{{\chi^{2}\lambda} = \frac{{counts} - {N\mspace{11mu} 1} - e^{- \lambda^{2}}}{{N\;\frac{counts}{N}\mspace{14mu} 1} - \frac{counts}{N}}} & (14)\end{matrix}$χ² is minimized when

$\begin{matrix}{\lambda = {{{- \ln}\mspace{11mu} 1} - \frac{counts}{N}}} & (15)\end{matrix}$

The variance in this most probable value for can be determined from thesecond derivative of χ² evaluated at the minimum.

$\begin{matrix}{\sigma^{2} = {2\;\frac{d^{2}\chi^{2}}{d\;\lambda^{2}}}} & (16)\end{matrix}$

While this method may be valid for counting measurements of any Poissondistributed process in the limit in which every initial event (e.g.,electron cascade) is guaranteed to generate a detected count, there aretwo practical considerations. First, any realistic detector will alsohave dark counts, (e.g., counts generated by spontaneous initiation ofan electron cascade). The dark counts may be subtracted from thecalculated value of λ in the low-count limit where they are asignificant influence. Second, even though this expression may properlycalculate the correct mean number of photons, μ_(P), the variance willbe slightly larger than the limiting value of σ_(P) ²=μ_(P) for valuesof μ_(P) outside the Poisson counting range. FIG. 3 illustrates thecalculated signal to noise ratio (μ/σ) as a function of the calculatedμ_(P) and a comparison to the ratio intrinsic in the Poissondistribution (i.e., the theoretical limit). The curve tracks closelywith the square root of counts for low counts (i.e. within Poissoncounting range). Indeed, this ratio is only a factor of 2 lower than thePoisson limit when the probability of observing a count exceeds ˜90% ofN.

The highest quantifiable mean number of photons is limited only by N.Depending on the precision required by the specific application, it isthe N−1 counts may be converted into the estimated number of incidentphotons. The maximum quantifiable number of photons is given by ln N.For example, this method would extend the linear range to ˜11 photonsper pulse, on average, if N=100,000 and there are negligible darkcounts.

It not always practical to simply increase N so as to extend the linearrange, since increasing N by a factor of 10 results in a 10-foldincrease in the measurement time with only a ln(10)=2.303 increase indynamic range. A method for further extension of the linear range is toset higher thresholds that will only ever be exceeded by manysimultaneous photon events. However, interpreting the results from suchhigher thresholds includes accounting for the lognormal peak voltagedistribution in the expression for p in (13). Specifically, p is givenby integration over the pdf of the voltage distribution (p=1−cdf,evaluated at that threshold value).

Representative curves that relate p and photons for various thresholdsare shown in FIG. 4, demonstrating the quantitative range for differentthreshold values. It may be possible to set a plurality of thresholdvoltages such that the linear dynamic range can be extended all the wayto (and perhaps beyond) the range available to signal averagingmeasurements, limited only by roll-off in the PMT linear response fromelectron depletion of the dynodes. In this analysis, the sameone-parameter weighted-nonlinear-fit for the mean of the Poissondistribution is used, except it is evaluated numerically.

In an example an apparatus generating visible light intensities over 8orders of magnitude is shown in FIG. 5, which depicts the optical setupused to generate these intensities by second harmonic generation (SHG).The apparatus includes a pulsed infrared laser 10, a Glan polarizer 20,1064 nm zeroth order halfwave plates 30 a visible wavelength blockingfilter 40, a KTP (Potassium Titanium Oxide Phosphate (KTiOPO₄) frequencydoubling crystal 50, and IR blocking filter 60 and a PMT 70.

A pulsed laser (JDS Nanolaser, 1064 nm, 6 kHz, 0.5 ps pulse duration, 40mW average power) was directed through two attenuators) before reachinga frequency doubling crystal (ALPHALAS, Goettingen, Germany). Theintensity of the light to be detected was controlled by selection of therotation angles of the zero-order halfwave plates 30 (calibrated towithin 0.1°) used in the attenuators. Each attenuator produced 4 ordersof magnitude variation in SHG intensity. With the incident lightattenuated as much as possible, the angle of incidence of the light onthe frequency doubling crystal was adjusted until the observed countswere well within Poisson counting range.

Two different photomultiplier tubes (PMT) were used. A Burle 8850(Lancaster, Pa.) was used in the confirmation of the log-normal pdf. APhotonis XP2920 (Merignac, France) was used for the extended linearrange. Becker-Hickel counting cards (MSA 1000) (Berlin, Germany) wereused for impulse counting with 1 ns bins, triggered from the laser. Forthe higher signal intensities, 10 dB attenuators were added to thesignal channel, to maintain the signal level within the range ofthresholds available from the counting cards. The signal averaging wasperformed using an oscilloscope (Tektronix 3054B, Beaverton, Wash.). Thedata analysis (e.g. single and multiple parameter weighted nonlinearfitting) was performed on software written in MathCad 14 (ParametricTechnology Corporation, Needham, Mass.) for this purpose using built-inmathematical functions to evaluate Poisson probabilities and log-normalcdf values.

The voltage distributions for various incident photon fluxes weremeasured and compared with the peak voltage distributions predicted by aweighted sum of related lognormal distributions. Integrals over thevoltage distributions were measured by setting successively higherthresholds and measuring the counts. The derivatives with respect to thethreshold were calculated to recover the voltage distributions. For theone-photon voltage distribution, a three parameter unweighted nonlinearfit was performed to determine λ, M_(L,1) and S_(L,1). The best fitvalue for ML,1 was −5.025 with a standard deviation of 0.019 and forSL,1 was 0.275 with a standard deviation of 0.021. Curve 100 in FIG. 6represents the voltage distribution within the Poisson counting limit(scaled up by a factor of 10 so as to be better viewed), where theprobability of observing two or more simultaneous photons per laserpulse was much less than one.

Setting M_(L,1) and S_(L,1) as constants allowed prediction of thelog-normal pdfs for an arbitrary number of photons. Using the mean ofthe Poisson distribution λ as the only adjustable parameter, fits weregenerated for λ=0.610 with a standard deviation of 0.029 for the upperset of points (curve 110), and λ=0.01333 with a standard deviation of0.00050.

Using only the M_(L,1) and S_(L,1) parameters for a given PMT, the meannumber of photons were calculated. The observed counts and thecalculated photons are compared in FIG. 7 as a function of the incidentintensity for the range just outside of Poisson counting range. ForN=10⁵, the Poisson limit is 1.03×10⁴ counts (λ=0.103). The highestobserved number average number of photons was 4.71×10⁵ (λ=4.71 with astandard deviation of 0.033), corresponding to a nearly 50-fold increasein linear range using the analytical single threshold method describedby (16). The signal to noise ratio at this extreme value was ˜5 timeslower than the theoretical maximum as estimated by Poisson statistics.

To extend the range further, several higher thresholds were chosen. Thecounts observed for the higher thresholds were converted to photons bynumerically minimizing χ²(λ) for each threshold. Only a single parameterwas used in the fits, since M_(L,1) and S_(L,1) were determinedexperimentally under conditions in which the Poisson and binomialdistributions converged. In addition, signal-averaging measurementsconsistent with analog-to-digital conversion (ADC) approaches wereperformed simultaneously with 512 averages. FIG. 8 shows these results.

The linear range for photon counting may be limited on the low end bythe dark count rate and on the high end, as discussed above, by thedeviation of the binomial distribution of counts and the Poissondistribution of photons (λ=0.103 or p=0.098). For the PMT used in FIGS.7 and 8, the dark count rate was 7.8×10⁻⁶ counts per laser pulse,yielding a linear range of ˜4 orders of magnitude using conventionalphoton counting approaches. The linear range for signal averaging waslimited on the low end by Johnson noise and on the high end by chargedepletion of the dynodes. The signal averaging data were rescaled tooverlay with the extrapolated linear fit from the SPC method forcomparison. In this experiment, the linear range for signal averagingbegins at ˜1 mV (λ=0.045) and extends to ˜890 mV (λ=45) providing ˜3orders of magnitude of linear range.

The analysis method described thus was has a linear range extending fromthe dark count rate all the way to saturation of the detector: ˜7 ordersof magnitude, spanning the entire range of both SPC and signalaveraging. A total of 6 thresholds were used to cover this range.Analysis of the counts from the first threshold provided ˜5.6 orders ofmagnitude and the other thresholds were used for the next ˜1.4 orders ofmagnitude. The average standard deviation of the calculated number ofphotons, above Poisson range and below saturation was 1.5 times thetheoretical limit.

Extending the multi-threshold counting approach to provide a continuouslinearity throughout the large intrinsic linear dynamic range of a PMT,hundreds of threshold may be used to maintain maximum signal-to-noiseratio over the entire measurement range. Realization of more thanhundreds of discrimination thresholds can be achieved in practice bydigitization of the peak voltage of each signal pulse using an ADC. Thismethod intrinsically results in hundreds or even thousands of“thresholds”.

The high-resolution voltage discrimination that digitization affordsleads to direct recovery of the voltage distributions, simplifying theanalysis to reduce the dependence on nonlinear curve fitting with widelyspaced discrimination levels. In addition, the voltage distributions forsingle signal events and for the noise can be continuously monitored inareas with little or no signal, instead of being measured once before atthe start of experiments. This allows for self-calibration of thedetector response and can provide access to the full dynamic range ofthe detector using analogous statistical methods to the multi-thresholdcounting method. An example of peak height distributions measured atsingle pixels during nonlinear optical imaging is shown in FIG. 9.

The approach uses the ability to digitize the peak of the signal voltagepulse by the ADC by clocking or triggering the ADC such that the voltageis sampled and digitized in time synchronism with the laser pulse. Inorder to perform these measurements with a detector with a response timeof about 1 ns, the bandwidth of the ADC sample and hold circuit shouldbe sufficient to respond to such short pulses. Using a 16-bit ADC, andan 80 MHz or 160 MHz laser repetition rate used in this example, withtwo channels of detection, the data rate is 640 MB/s. A PCIe or othermodern bus can transfer this amount of data to computer memory. Forcontinuous acquisitions, the data is processed or saved to non-volatilememory faster than they are acquired. For the analysis methods describedherein, specifically linear fitting, a graphics processing unit (GPU)may be used increase the processing speed for performing real-time dataanalysis.

In an aspect, a method for using digitized data to perform counting,using an analytical counting linearization relationship, and a signalaveraging simultaneously. Counting is performed by comparing eachdigitized value to a threshold based on the highest voltage expectedfrom the noise. Those same voltages measured for each laser pulse canthen be averaged. The counting results are used in areas of low signaland the signal averaging results are used for high signal. The voltageis related to the number of photons incident on a detector by comparingthe results for counting and averaging in the regime where the averagenumber of signal generating events per laser pulse is about 1.

This method is self-calibrating because the counting threshold can beadjusted as the noise changes and the relationship between averagevoltage and number of signal generating events is continuously adjustedin case the detector response characteristics change.

Second harmonic generation microscopy images resulting from thisanalysis method are shown in FIG. 10. These results were acquired with a160 MHz laser (1000 nm, 100 mw, Spectraphysics MaiTai, Santa Clara,Calif.) whose repetition rate had been doubled. The digitizer wasclocked to that repetition rate and 16-bit samples were recorded on twochannels simultaneously (AlazarTech ATS9462, Pointe-Claire, QC, Canada).The entire image required about 3 seconds to acquire using a rasterscanning technique. Stitching of counting and averaging images in thisway requires overlap between the dynamic ranges of detection for bothtechniques, which was enabled in this case through the use thealgorithms given in (12) and (13) connecting the Poisson and binomialdistributions for extending the dynamic range of discrimination-basedcounting.

Since the entire distribution of peak voltages is available in eachpixel of the measurement (FIG. 9), it is possible to perform a linearfit to the voltage distributions of the pixel. The noise and signalvoltage distributions are measured in a similar way, as has previouslydescribed. After each section of data is acquired, the data can betransferred to a GPU. Linear curve fitting to the measured voltagedistributions are then calculated. The weighted mean and variance of theunderlying Poisson distribution of counting events per excitation event(in this case, per laser pulse) are calculated for the linear fittingcoefficients and may be returned as the results in real time.

The examples provided herein us detection of light generated from pulsedexcitation. However, there are applications for this method that do notinvolve signals timed to a pulsed laser, such as mass spectrometry andmany fluorescence measurements. A problem associated with non-pulsedapplications is that it can no longer be assured that the signal that isrecorded corresponds to the peak of the signal voltage pulse. There mayalso be the potential of overlap between signal pulses shifted in time.The measured voltage distribution will be complicated by the temporalresponse of the signal pulse and the temporal overlap such that therelationship between the single event response and the higher number ofevent responses will be unclear.

Precise knowledge of the temporal response of the detector maycomplicates algorithms based on linear fitting of the peak heightdistribution, but still allows data analysis based on connecting theanalytical counting linearization and signal averaging. When using theanalytical counting approach, the time between digitizations should begreater than twice the rise and fall time of the detector. In this case,no signal will appear on two different digital measurements. The signalaveraging lower detection limit is also raised slightly due to theincreased variance in the signal. This data analysis method allows thesame electronics to be used for collection and analysis pulsed andnon-pulsed signals.

Since this method can be used with pulsed signals or non-pulsed signals,it can be applied to many other experiments that use similar types ofdetectors and can have similar dynamic range issues. For example, massspectrometers frequently use ion-multiplying tubes to generate shortpulsed signals when an ion strikes the face of the detector. This allowsfor choosing to count ions or average the voltages they generate in ananalogous way that photons are detected by PMTS.

When data acquisition is performed with a small number (<20) ofdiscriminator measurements, rather than direct digitization of theindividual voltage transients, the approach described herein needs toaccommodate practical aspects of the measurement devices. Ringing fromslight mismatches in impedance can result in multiple counts from asingle event (i.e., after-pulsing). However, time gating and/or carefulimpedance matching can usually alleviate this issue. At high pulserepetition with high rates, the time between pulses may be relativelyshort, and the voltage transient may not recover to baseline before thenext transient is generated. In this case, thresholds set to lowmagnitudes may not count the next pulse, biasing the measured counts.This problem may be mitigated, for example, by increasing the timebetween pulses, reducing the response time of the detector, orcorrecting the low-threshold data for this effect. Direct digitizationlargely alleviates complications from after-pulsing as well as drift inthe DC background and reduces complications from the detector responsetime.

Gradual drift in the gain (i.e. M_(L,1) and S_(L,1)) can also adverselyaffect the analysis based on discriminator processing alone. The singlethreshold method is relatively insensitive to this problem as long asthe detector gain does not drop low enough that the probability of apulse not exceeding the threshold becomes significant. However, themulti-threshold method explicitly relies on accurate characterization ofthe gain distribution and could produce different results if there is asignificant discrepancy between the assumed and actual gain. Thestability of a PMT can be characterized before applying methods forquantization of voltages. Alternatively, direct digitization provides aroute for experimentally measuring the peak-height distribution directlyto enable dynamic corrections based on the measured instrument response.

Three related and complementary statistical approaches were demonstratedfor extending the linear range in photon counting measurements ofcoincident photons (e.g., using pulsed laser excitation). A simpleexpression relating observed counts and mean number of photons wasderived to extend the linear counting range past the traditional limiton photon counting. A more general form of this expression based oncombined results from multiple-threshold discrimination can be used tonumerically extend the linear range to the limit where the PMT voltageresponse departs from linearity due to electron depletion of thedynodes. Further extension of the multi-threshold measurement to directdigitization of each voltage transient can enable continuousoptimization of signal-to-noise ratio throughout the entire intrinsiclinear dynamic range of the detector. Direct digitization can alsoenable real-time measurement of the DC offset, background Johnson noisecharacteristics, and the detector response function to allow real-timeoptimization of the statistical analysis.

A method for statistically analyzing the data acquired by usingdiscriminator-based counting electronics to measure a number of signalgenerating events on a short pulsed detector includes relating thebinomially distributed measurement of counts and the Poisson distributedsignal of discreet events, using nonlinear curve fitting to a Poissonweighted sum of related detector response functions to relate thebinomially distributed measurement of counts and the Poisson distributedsignal of discreet events.

In an aspect, a method for measuring a range of voltages from a detectorin pulsed signal measurements includes: digitizing the signals from thedetector, synchronizing the digitization to the times when the signal isexpected, and performing statistical analysis of the distribution ofdigitized signals to connect the binomial and Poisson distributions toconnect the dynamic ranges of counting and signal averaging.

In another aspect, a method for measuring a signal from a singledetector in continuous signal measurements includes: digitizing thesignals from a short pulse detector and performing statistical analysisof the distribution of digitized signals to connect the binomial andPoisson distributions to connect the dynamic ranges of counting andsignal averaging.

In yet another aspect, a method for measuring a range of voltages from asingle detector in pulsed signal measurements includes: digitizing thesignals from a detector, synchronizing the digitization to the timeswhen the signal is expected, and performing linear fitting of themeasured peak height voltage distribution to a linear combination ofknown functions, comprising the expected peak height distributions ofthe detector as a function of the number of signal generating events andthe electronic noise.

While the methods disclosed herein have been described and shown withreference to particular steps performed in a particular order, it willbe understood that these steps may be combined, sub-divided, orreordered to from an equivalent method without departing from theteachings of the present invention. Accordingly, unless specificallyindicated herein, the order and grouping of steps is not a limitation ofthe present invention.

Although only a few examples of this invention have been described indetail above, those skilled in the art will readily appreciate that manymodifications are possible without materially departing from the novelteachings and advantages of the invention. Accordingly, all suchmodifications are intended to be included within the scope of thisinvention as defined in the following claims.

What is claimed is:
 1. An apparatus for measuring a large dynamic rangeof light intensity, comprising: a pulsed light source; a detector forreceiving light energy; an analog-to-digital converter having a samplingrate synchronized with the pulsed light source, digitizing a voltageoutput of the detector; a processor configured to accept output data ofthe analog-to-digital converter for a plurality of light pulses and toprocess the output data so as to compute an average-count-type datavalue obtained by threshold processing of the detector output, and anaverage signal-type-data value obtained by averaging the detectorvoltage output for the same received light pulses, wherein the processoris further configured to use the average-count-type data value where theaverage-count-type data value is less than about 1 and to use theaverage-signal-type data value where the average count-type data valueis greater than about 1 as a measure of the light intensity.
 2. Theapparatus of claim 1, wherein the transition point is selected based tooptimize a signal-to-noise ratio over the range of the count-type dataand the average-signal-type data threshold value is a peak voltageoutput of the detector when there is no light from the light sourceincident on the detector.
 3. A computer program product, stored on anon-transient computer readable medium, comprising: instructions foroperating a processor of a photon counting system so as to perform ameasurement of a value of light intensity incident on a detector byperforming the steps of: synchronizing analog-to-digital conversion of avoltage output of the detector with a light source such thatanalog-to-digital conversion is performed at a time where the voltageoutput from the detector is expected and outputtinganalog-digital-conversion data values; accepting analog-to-digitalconversion (ADC) data values; processing the accepted ADC data values soas to compute an average-count-type data value and anaverage-voltage-type data value, where the average-count-type data valueis the average number of ADC output data values above a threshold valueper ADC conversion and the average-voltage-type data value is theaverage of the ADC output data values above the threshold value per ADCconversion and the average-count-type data value and theaverage-voltage-type data is computed for the same group of ADCconversion output data values, wherein the average-count-type data valueis used to represent the light intensity value when theaverage-count-type data value is less than about 1 count per pulse, andthe average-signal-type data value is used where the average-count-typedata value is greater than about 1 count per pulse; and, outputting thelight intensity value on a display device.
 4. The computer programproduct of claim 3, wherein a plurality of accepted data is stored priorto performing the process.